Temporal vs. Parietal Cortex

The two competing hypotheses about cortical function are based on the differences between positive and negative feedback signals. Both of these types of feedback may be useful for different cortical areas, and they may have deeper relations. ART (Grossberg) describes the mechanisms of the positive feedback system, where predictive-coding (Maass) describes the negative feedback system.

We know that the visual system is basically split up into the "What" pathway and the "Where" pathway. The "What" pathway goes down temporal cortex, and as you move from V1 to IT neurons become object recognizers and lose spatial invariance. Going up the "Where" pathway (this is less studied) neurons are binding the objects to information about their spatial properties (position, movement, momentum etc).

So it seems that up the parietal cortex, having a system that can predict the future, and is constantly minimizing error signals based on these predictions would be very powerful. The negative feedback seems like an ideal way to derive the laws of mechanics and understand how objects move throughout the world. Grossberg says that you need resonance to have consciousness. This seems to fit as you are not really conscious of parietal cortex (your conscious mind does not have as much spatial information as your subconscious motor system).

Temporal cortex, however, is part of your conscious awareness. This is because, according to Grossberg, there are resonant states being created by the positive feedback system (you are not necessarily conscious of all resonance states). Resonant states are a binding of the data with a representation, this binding is the key to conscious awareness. The negative-feedback states are not bound - they propagating prediction errors.

Both types of these feedback systems will have rules that can be based on the two-input pyramidal cell model. Each pyramidal cell can receive different types of inputs - bottom-up (data) inputs through the basal tree, and top-down (symbol/class) inputs through the apical tree. Plasticity rules can be established to create both the positive and negative feedback systems.

The positive feedback system will strengthen synapses when the symbols are a good match to the data. The synapses will grow in strength each time a pattern is introduced, limited by some maximum strength or a normalization procedure.

The negative feedback system will compare a top-down prediction with a bottom-up state, and use the difference to modulate synaptic strengths. This will create negative loops and the system will be driven to local minimas.